Main

Step 0 set work directories

set.seed(2020)
# setwd("~/Project3-FacialEmotionRecognition/doc")
# here replace it with your own path or manually set it in RStudio to where this rmd file is located. 
# use relative path for reproducibility

Provide directories for training images. Training images and Training fiducial points will be in different subfolders.

train_dir <- "../data/train_set/" # This will be modified for different data sets.
train_image_dir <- paste(train_dir, "images/", sep="")
train_pt_dir <- paste(train_dir,  "points/", sep="")
train_label_path <- paste(train_dir, "label.csv", sep="")

Step 1: set up controls for evaluation experiments.

In this chunk, we have a set of controls for the evaluation experiments.

(T/F) cross-validation on the training set
(T/F) reweighting the samples for training set
(number) K, the number of CV folds
(T/F) process features for training set
(T/F) run evaluation on an independent test set
(T/F) process features for test set

# run.cv <- TRUE # run cross-validation on the training set
# sample.reweight <- TRUE # run sample reweighting in model training
# K <- 5  # number of CV folds
# run.feature.train <- TRUE # process features for training set
# run.test <- TRUE # run evaluation on an independent test set
# run.feature.test <- TRUE # process features for test set

sample.reweight <- TRUE # run sample reweighting in model training
K <- 5  # number of CV folds
run.feature.train <- TRUE # process features for training set
run.feature.test <- TRUE # process features for test set

run.cv_gbm <- TRUE # run GBM cross-validation on the training set
run.test_gbm <- TRUE # run GBM evaluation on an independent test set

run.cv_dnn <- TRUE # run DNN cross-validation on the training set
run.test_dnn <- TRUE # run DNN evaluation on an independent test set

Using cross-validation or independent test set evaluation, we compare the performance of models with different specifications. In this part, we tune parameter n.trees and shrinkage for GBM.

# GBM parameters
n.trees  <- c(500, 100, 1500)
shrinkage <- c(0.01, 0.05, 0.1)

Step 2: import data and train-test split

#train-test split
info <- read.csv(train_label_path)
n <- nrow(info)
n_train <- round(n*(4/5), 0)
train_idx <- sample(info$Index, n_train, replace = F)
test_idx <- setdiff(info$Index, train_idx)

If you choose to extract features from images, such as using Gabor filter, R memory will exhaust all images are read together. The solution is to repeat reading a smaller batch(e.g 100) and process them.

n_files <- length(list.files(train_image_dir))

image_list <- list()
for(i in 1:100){
   image_list[[i]] <- readImage(paste0(train_image_dir, sprintf("%04d", i), ".jpg"))
}

Fiducial points are stored in matlab format. In this step, we read them and store them in a list.

#function to read fiducial points
#input: index
#output: matrix of fiducial points corresponding to the index
readMat.matrix <- function(index){
     return(round(readMat(paste0(train_pt_dir, sprintf("%04d", index), ".mat"))[[1]],0))
}

#load fiducial points
fiducial_pt_list <- lapply(1:n_files, readMat.matrix)

strings not representable in native encoding will be translated to UTF-8

save(fiducial_pt_list, file="../output/fiducial_pt_list.RData")

Step 3: construct features and responses

The follow plots show how pairwise distance between fiducial points can work as feature for facial emotion recognition.
- In the first column, 78 fiducials points of each emotion are marked in order.
- In the second column distributions of vertical distance between right pupil(1) and right brow peak(21) are shown in histograms. For example, the distance of an angry face tends to be shorter than that of a surprised face.
- The third column is the distributions of vertical distances between right mouth corner(50) and the midpoint of the upper lip(52). For example, the distance of an happy face tends to be shorter than that of a sad face.

Figure1

feature.R should be the wrapper for all your feature engineering functions and options. The function feature( ) should have options that correspond to different scenarios for your project and produces an R object that contains features and responses that are required by all the models you are going to evaluate later.

feature.R
Input: list of images or fiducial point
Output: an RData file that contains extracted features and corresponding responses

source("../lib/feature.R")
tm_feature_train <- NA
if(run.feature.train){
  tm_feature_train <- system.time(dat_train <- feature(fiducial_pt_list, train_idx))
  save(dat_train, file="../output/feature_train.RData")
}else{
  load(file="../output/feature_train.RData")
}

tm_feature_test <- NA
if(run.feature.test){
  tm_feature_test <- system.time(dat_test <- feature(fiducial_pt_list, test_idx))
  save(dat_test, file="../output/feature_test.RData")
}else{
  load(file="../output/feature_test.RData")
}

GBM

Step 4: Train a classification model with training features and responses

Call the train model and test model from library.

train.R and test.R should be wrappers for all your model training steps and your classification/prediction steps.

train.R
- Input: a data frame containing features and labels and a parameter list.
- Output:a trained model
test.R
- Input: the fitted classification model using training data and processed features from testing images
- Input: an R object that contains a trained classifier.
- Output: training model specification
In this part, we use GBM (baseline model) to do classification.

source("../lib/train_gbm.R") 
source("../lib/test_gbm.R")

Model selection with cross-validation

Do model selection by choosing among different values of training model parameters.

source("../lib/cross_validation_gbm.R")
feature_train = as.matrix(dat_train[, -6007])
label_train = as.integer(dat_train$label)
if(run.cv_gbm){
  res_cv_gbm <- matrix(0, nrow = length(n.trees) * length(shrinkage), ncol = 6)
  count = 0
  for(i in 1:length(n.trees)){
    for(j in 1:length(shrinkage)){
      count = count + 1
      cat("n.trees =", n.trees[i], "\n")
      cat("shrinkage =", shrinkage[j], "\n")
      res_cv <- cv.function_gbm(features = feature_train, labels = label_train, K,
                              n.trees[i], shrinkage[j], reweight = sample.reweight)
      
      res_cv_gbm[count,] <- c(n.trees[i], shrinkage[j], res_cv[1], res_cv[2], res_cv[3], res_cv[4])
    }
  }
  
  colnames(res_cv_gbm) <- c("n.trees","shrinkage","mean_error", "sd_error", "mean_AUC", "sd_AUC")
  save(res_cv_gbm, file="../output/res_cv_gbm.RData")
}else{
  load("../output/res_cv_gbm.RData")
}

n.trees = 500 
shrinkage = 0.01 
n.trees = 500 
shrinkage = 0.05 
n.trees = 500 
shrinkage = 0.1 
n.trees = 100 
shrinkage = 0.01 
n.trees = 100 
shrinkage = 0.05 
n.trees = 100 
shrinkage = 0.1 
n.trees = 1500 
shrinkage = 0.01 
n.trees = 1500 
shrinkage = 0.05 
n.trees = 1500 
shrinkage = 0.1

Visualize cross-validation results.

res_cv_gbm <- as.data.frame(res_cv_gbm) 

if(run.cv_gbm){
  p1 <- res_cv_gbm %>% 
    ggplot(aes(x = n.trees, y = mean_error,
               ymin = mean_error - sd_error, ymax = mean_error + sd_error)) + 
    geom_crossbar() +
    facet_wrap(~shrinkage)+
    theme(axis.text.x = element_text(angle = 90, hjust = 1))
  
  p2 <- res_cv_gbm %>% 
    ggplot(aes(x = n.trees, y = mean_AUC,
               ymin = mean_AUC - sd_AUC, ymax = mean_AUC + sd_AUC)) + 
    geom_crossbar() +
    facet_wrap(~shrinkage)+
    theme(axis.text.x = element_text(angle = 90, hjust = 1))
  
  print(p1)
  print(p2)
}

Choose the “best” parameter value

# par_n.trees_best <- as.numeric(res_cv_gbm[which.min(res_cv_gbm$mean_error), 1])
# par_shrinkage_best <- as.numeric(res_cv_gbm[which.min(res_cv_gbm$mean_error), 2])

par_n.trees_best <- 500
par_shrinkage_best <- 0.05

Train the model with the entire training set using the selected model (model parameter) via cross-validation.

# training weights
weight_train <- rep(NA, length(label_train))
for (v in unique(label_train)){
  weight_train[label_train == v] = 0.5 * length(label_train) / length(label_train[label_train == v])
}
if (sample.reweight){
  tm_train <- system.time(fit_train <- train_gbm(feature_train, label_train, w = weight_train, par_n.trees_best, par_shrinkage_best))
} else {
  tm_train <- system.time(fit_train <- train_gbm(feature_train, label_train, w = NULL, par_n.trees_best, par_shrinkage_best))
}
save(fit_train, file="../output/fit_train_gbm.RData")

Step 5: Run test on test images

tm_test = NA
feature_test <- as.matrix(dat_test[, -6007])
if(run.test_gbm){
  load(file="../output/fit_train_gbm.RData")
  tm_test <- system.time({prob_pred <- test_gbm(fit_train, feature_test, par_n.trees_best, pred.type = 'response');})
}

evaluation

label_test <- as.integer(dat_test$label)

# DELETE
save(feature_test, file="../output/feature_test_tmp_only.RData")

Error in save(feature_test, file = "../output/feature_test_tmp_only.RData") : 
  目标对象‘feature_test’不存在

Summarize Running Time

Prediction performance matters, so does the running times for constructing features and for training the model, especially when the computation resource is limited.

cat("Time for constructing training features=", tm_feature_train[1], "s \n")

Time for constructing training features= 0.64 s

cat("Time for constructing testing features=", tm_feature_test[1], "s \n")

Time for constructing testing features= 0.12 s

cat("Time for training model=", tm_train[1], "s \n")

Time for training model= 80.55 s

cat("Time for testing model=", tm_test[1], "s \n")

Time for testing model= 0.12 s

XGBoost

Step 4: Train a classification model with training features and responses

Call the train model and test model from library.

train.R and test.R should be wrappers for all your model training steps and your classification/prediction steps.

train.R
- Input: a data frame containing features and labels and a parameter list.
- Output:a trained model
test.R
- Input: the fitted classification model using training data and processed features from testing images
- Input: an R object that contains a trained classifier.
- Output: training model specification
In this part, we use GBM (baseline model) to do classification.

source("../lib/train_xgboost.R") 
source("../lib/test_xgboost.R")

Model selection with cross-validation

Do model selection by choosing among different values of training model parameters.

source("../lib/cross_validation_xgboost.R")
feature_train = as.matrix(dat_train[, -6007])
label_train = as.integer(dat_train$label)

if(run.cv_xgboost){
  res_cv_xgboost <- matrix(0, nrow = length(eta) * length(colsample_bytree), ncol = 6)
  count = 0
  for(i in 1:length(eta)){
    for(j in 1:length(colsample_bytree)){
      count = count + 1
      cat("eta =", eta[i], "\n")
      cat("colsample_bytree =", colsample_bytree[j], "\n")
      
      res_cv <- cv.function_xgboost(features = feature_train, labels = label_train, K,
                              sample_weight, eta[i], colsample_bytree[j], reweight = sample.reweight)
      
      res_cv_xgboost[count,] <- c(eta[i], colsample_bytree[j], res_cv[1], res_cv[2], res_cv[3], res_cv[4])
    }
  }
  
  colnames(res_cv_gbm) <- c("eta","colsample_bytree","mean_error", "sd_error", "mean_AUC", "sd_AUC")
  save(res_cv_gbm, file="../output/res_cv_xgboost.RData")
}else{
  load("../output/res_cv_xgboost.RData")
}

Visualize cross-validation results.

res_cv_xgboost <- as.data.frame(res_cv_xgboost) 

if(run.cv_xgboost){
  p1 <- res_cv_xgboost %>% 
    ggplot(aes(x = eta, y = mean_error,
               ymin = mean_error - sd_error, ymax = mean_error + sd_error)) + 
    geom_crossbar() +
    facet_wrap(~colsample_bytree)+
    theme(axis.text.x = element_text(angle = 90, hjust = 1))
  
  p2 <- res_cv_xgboost %>% 
    ggplot(aes(x = eta, y = mean_AUC,
               ymin = mean_AUC - sd_AUC, ymax = mean_AUC + sd_AUC)) + 
    geom_crossbar() +
    facet_wrap(~colsample_bytree)+
    theme(axis.text.x = element_text(angle = 90, hjust = 1))
  
  print(p1)
  print(p2)
}

Choose the “best” parameter value

# par_n.trees_best <- as.numeric(res_cv_gbm[which.min(res_cv_gbm$mean_error), 1])
# par_shrinkage_best <- as.numeric(res_cv_gbm[which.min(res_cv_gbm$mean_error), 2])

par_eta_best <- 0.1
par_colsample_bytree_best <- 0.6

Train the model with the entire training set using the selected model (model parameter) via cross-validation.

# training weights
weight_train <- rep(NA, length(label_train))
for (v in unique(label_train)){
  weight_train[label_train == v] = 0.5 * length(label_train) / length(label_train[label_train == v])
}

tm_train <- NA
if (sample.reweight){
  tm_train <- system.time(fit_train <- train_xgboost(feature_train, label_train, sample_weight, par_eta_best, par_colsample_bytree_best))
} else {
  tm_train <- system.time(fit_train <- train_xgboost(feature_train, label_train, sample_weight = NULL, par_eta_best, par_colsample_bytree_best))
}
xgb.save(fit_train, "../output/fit_train_xgboost.model")

Step 5: Run test on test images

tm_test = NA
feature_test <- as.matrix(dat_test[, -6007])
if(run.test_xgboost){
  fit_train <- xgb.load("../output/fit_train_xgboost.model")
  tm_test <- system.time({prob_pred <- test_xgboost(fit_train, feature_test, pred.type = 'probability');})
}

evaluation

## reweight the test data to represent a balanced label distribution
label_test <- as.integer(dat_test$label)

weight_test <- rep(NA, length(label_test))
for (v in unique(label_test)){
  weight_test[label_test == v] = 0.5 * length(label_test) / length(label_test[label_test == v])
}

label_pred <- ifelse(prob_pred > 0.5, 1, 0)
label_test <- ifelse(label_test == 2, 0, 1)

accu <- sum(weight_test * (label_pred == label_test)) / sum(weight_test)
tpr.fpr <- WeightedROC(prob_pred, label_test, weight_test)
auc <- WeightedAUC(tpr.fpr)


cat("The accuracy of model:", "XGBoost with eta =" , par_eta_best, "and colsample_bytree =", par_colsample_bytree_best, "is", accu*100, "%.\n")
cat("The AUC of model:", "XGBoost with eta =" , par_eta_best, "and colsample_bytree =", par_colsample_bytree_best, "is", auc, ".\n")

Summarize Running Time

Prediction performance matters, so does the running times for constructing features and for training the model, especially when the computation resource is limited.

cat("Time for constructing training features=", tm_feature_train[3], "s \n")
cat("Time for constructing testing features=", tm_feature_test[3], "s \n")
cat("Time for training model=", tm_train[3], "s \n") 
cat("Time for testing model=", tm_test[3], "s \n")

DNN

Step 4: Train a classification model with training features and responses

Call the train model and test model from library.

train.R and test.R should be wrappers for all your model training steps and your classification/prediction steps.

train.R
- Input: a data frame containing features and labels and a parameter list.
- Output:a trained model
test.R
- Input: the fitted classification model using training data and processed features from testing images
- Input: an R object that contains a trained classifier.
- Output: training model specification
In this part, we use GBM (baseline model) to do classification.

source("../lib/train_dnn.R") 
source("../lib/test_dnn.R")

Model selection with cross-validation

Do model selection by choosing among different values of training model parameters.

source("../lib/cross_validation_dnn.R")
feature_train = as.matrix(dat_train[, -6007])
label_train = as.integer(dat_train$label)

# if(run.cv_dnn){
#   res_cv_dnn <- matrix(0, 1, ncol = 4)
#   
#   res_cv <- cv.function_dnn(features = feature_train, labels = label_train, K,
#                           reweight = sample.reweight)
#   
#   res_cv_dnn[1,] <- c(res_cv[1], res_cv[2], res_cv[3], res_cv[4])
#   
#   colnames(res_cv_dnn) <- c("mean_error", "sd_error", "mean_AUC", "sd_AUC")
#   save(res_cv_dnn, file="../output/res_cv_dnn.RData")
# }else{
#   load("../output/res_cv_dnn.RData")
# }

Visualize cross-validation results.

# res_cv_dnn <- as.data.frame(res_cv_dnn) 
# 
# if(run.cv_dnn){
#   p1 <- res_cv_dnn %>% 
#     ggplot(aes(x = "DNN", y = mean_error,
#                ymin = mean_error - sd_error, ymax = mean_error + sd_error)) + 
#     geom_crossbar() +
#     theme(axis.text.x = element_text())
#   
#   p2 <- res_cv_dnn %>% 
#     ggplot(aes(x = "DNN", y = mean_AUC,
#                ymin = mean_AUC - sd_AUC, ymax = mean_AUC + sd_AUC)) + 
#     geom_crossbar() +
#     theme(axis.text.x = element_text())
#   
#   print(p1)
#   print(p2)
# }

Choose the “best” parameter value

# par_n.trees_best <- as.numeric(res_cv_gbm[which.min(res_cv_gbm$mean_error), 1])
# par_shrinkage_best <- as.numeric(res_cv_gbm[which.min(res_cv_gbm$mean_error), 2])

Train the model with the entire training set using the selected model (model parameter) via cross-validation.

source("../lib/train_dnn.R") 
source("../lib/test_dnn.R")

feature_train = as.matrix(dat_train[, -6007])
label_train = as.integer(dat_train$label)
feature_test <- as.matrix(dat_test[, -6007])
label_test <- as.integer(dat_test$label)
label_test <- ifelse(label_test == 2, 0, 1)
weight_test <- rep(NA, length(label_test))

# training weights
weight_train <- rep(NA, length(label_train))
for (v in unique(label_train)){
  weight_train[label_train == v] = 0.5 * length(label_train) / length(label_train[label_train == v])
}

for (v in unique(label_test)){
  weight_test[label_test == v] = 0.5 * length(label_test) / length(label_test[label_test == v])
}

# validation_split <- 1 / 5
# validation_split <- 0
tm_train <- NA
if (sample.reweight){
  tm_train <- system.time(fit_train <- train_dnn(feature_train, label_train, w = weight_train, feature_test, label_test, weight_test))
} else {
  tm_train <- system.time(fit_train <- train_dnn(feature_train, label_train, w = NULL, feature_test, label_test, weight_test = NULL))
}

Epoch 1/100

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480/480 [==============================] - 8s 18ms/step - loss: 0.7930 - accuracy: 0.5083

480/480 [==============================] - 9s 20ms/step - loss: 0.7930 - accuracy: 0.5083 - val_loss: 0.7051 - val_accuracy: 0.7767
Epoch 2/100

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480/480 [==============================] - 9s 19ms/step - loss: 0.7034 - accuracy: 0.5562

480/480 [==============================] - 9s 20ms/step - loss: 0.7034 - accuracy: 0.5562 - val_loss: 0.6725 - val_accuracy: 0.4133
Epoch 3/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.6768 - accuracy: 0.5858

480/480 [==============================] - 11s 22ms/step - loss: 0.6768 - accuracy: 0.5858 - val_loss: 0.6593 - val_accuracy: 0.6300
Epoch 4/100

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480/480 [==============================] - 10s 22ms/step - loss: 0.6563 - accuracy: 0.6067 - val_loss: 0.6853 - val_accuracy: 0.7267
Epoch 5/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.6522 - accuracy: 0.5921

480/480 [==============================] - 11s 22ms/step - loss: 0.6522 - accuracy: 0.5921 - val_loss: 0.6201 - val_accuracy: 0.6250
Epoch 6/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.6404 - accuracy: 0.6121

480/480 [==============================] - 11s 22ms/step - loss: 0.6404 - accuracy: 0.6121 - val_loss: 0.5875 - val_accuracy: 0.6550
Epoch 7/100

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480/480 [==============================] - 11s 22ms/step - loss: 0.6331 - accuracy: 0.6108 - val_loss: 0.5884 - val_accuracy: 0.6050
Epoch 8/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.6282 - accuracy: 0.6363

480/480 [==============================] - 11s 22ms/step - loss: 0.6282 - accuracy: 0.6363 - val_loss: 0.6373 - val_accuracy: 0.7350
Epoch 9/100

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480/480 [==============================] - 11s 23ms/step - loss: 0.6321 - accuracy: 0.6288 - val_loss: 0.5855 - val_accuracy: 0.6917
Epoch 10/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.6210 - accuracy: 0.6650

480/480 [==============================] - 11s 22ms/step - loss: 0.6210 - accuracy: 0.6650 - val_loss: 0.6216 - val_accuracy: 0.6383
Epoch 11/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.6280 - accuracy: 0.6654

480/480 [==============================] - 11s 22ms/step - loss: 0.6280 - accuracy: 0.6654 - val_loss: 0.5626 - val_accuracy: 0.6500
Epoch 12/100

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480/480 [==============================] - 11s 22ms/step - loss: 0.6114 - accuracy: 0.6837 - val_loss: 0.5673 - val_accuracy: 0.7433
Epoch 13/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.6075 - accuracy: 0.6833

480/480 [==============================] - 11s 22ms/step - loss: 0.6075 - accuracy: 0.6833 - val_loss: 0.5768 - val_accuracy: 0.7250
Epoch 14/100

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480/480 [==============================] - 11s 22ms/step - loss: 0.5936 - accuracy: 0.6608 - val_loss: 0.6230 - val_accuracy: 0.7700
Epoch 15/100

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480/480 [==============================] - 11s 22ms/step - loss: 0.5923 - accuracy: 0.6754 - val_loss: 0.5426 - val_accuracy: 0.7567
Epoch 16/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.5849 - accuracy: 0.6783

480/480 [==============================] - 11s 22ms/step - loss: 0.5849 - accuracy: 0.6783 - val_loss: 0.5841 - val_accuracy: 0.7100
Epoch 17/100

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480/480 [==============================] - 11s 23ms/step - loss: 0.5824 - accuracy: 0.7017 - val_loss: 0.5306 - val_accuracy: 0.7567
Epoch 18/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.5848 - accuracy: 0.7113

480/480 [==============================] - 11s 22ms/step - loss: 0.5848 - accuracy: 0.7113 - val_loss: 0.5375 - val_accuracy: 0.7517
Epoch 19/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.5943 - accuracy: 0.6587

480/480 [==============================] - 11s 22ms/step - loss: 0.5943 - accuracy: 0.6587 - val_loss: 0.5768 - val_accuracy: 0.7217
Epoch 20/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.5720 - accuracy: 0.7146 - val_loss: 0.5224 - val_accuracy: 0.7233
Epoch 21/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.5614 - accuracy: 0.7175

480/480 [==============================] - 10s 22ms/step - loss: 0.5614 - accuracy: 0.7175 - val_loss: 0.5628 - val_accuracy: 0.7383
Epoch 22/100

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480/480 [==============================] - 11s 22ms/step - loss: 0.5753 - accuracy: 0.6792 - val_loss: 0.7865 - val_accuracy: 0.7250
Epoch 23/100

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480/480 [==============================] - 10s 20ms/step - loss: 0.6243 - accuracy: 0.6562

480/480 [==============================] - 10s 22ms/step - loss: 0.6243 - accuracy: 0.6562 - val_loss: 0.5649 - val_accuracy: 0.7333
Epoch 24/100

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480/480 [==============================] - 10s 20ms/step - loss: 0.5627 - accuracy: 0.6717

480/480 [==============================] - 10s 22ms/step - loss: 0.5627 - accuracy: 0.6717 - val_loss: 0.5642 - val_accuracy: 0.7033
Epoch 25/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.5517 - accuracy: 0.6896 - val_loss: 0.6029 - val_accuracy: 0.7283
Epoch 26/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.5749 - accuracy: 0.6975 - val_loss: 0.5833 - val_accuracy: 0.6900
Epoch 27/100

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480/480 [==============================] - 10s 20ms/step - loss: 0.5859 - accuracy: 0.6625

480/480 [==============================] - 10s 21ms/step - loss: 0.5859 - accuracy: 0.6625 - val_loss: 0.6046 - val_accuracy: 0.7333
Epoch 28/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.6035 - accuracy: 0.6725 - val_loss: 0.5861 - val_accuracy: 0.6967
Epoch 29/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.5739 - accuracy: 0.6800 - val_loss: 0.5306 - val_accuracy: 0.6917
Epoch 30/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.5432 - accuracy: 0.7025 - val_loss: 0.6863 - val_accuracy: 0.7900
Epoch 31/100

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480/480 [==============================] - 10s 22ms/step - loss: 0.5393 - accuracy: 0.7292 - val_loss: 0.5143 - val_accuracy: 0.7417
Epoch 32/100

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480/480 [==============================] - 10s 20ms/step - loss: 0.5528 - accuracy: 0.7046

480/480 [==============================] - 10s 21ms/step - loss: 0.5528 - accuracy: 0.7046 - val_loss: 0.9424 - val_accuracy: 0.7667
Epoch 33/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.5562 - accuracy: 0.6842 - val_loss: 0.5781 - val_accuracy: 0.7683
Epoch 34/100

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480/480 [==============================] - 10s 20ms/step - loss: 0.5519 - accuracy: 0.7267

480/480 [==============================] - 10s 21ms/step - loss: 0.5519 - accuracy: 0.7267 - val_loss: 0.5499 - val_accuracy: 0.8000
Epoch 35/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.5691 - accuracy: 0.7362 - val_loss: 0.6278 - val_accuracy: 0.7983
Epoch 36/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.5423 - accuracy: 0.7246 - val_loss: 0.5794 - val_accuracy: 0.7050
Epoch 37/100

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480/480 [==============================] - 10s 20ms/step - loss: 0.5254 - accuracy: 0.7154

480/480 [==============================] - 10s 21ms/step - loss: 0.5254 - accuracy: 0.7154 - val_loss: 0.5433 - val_accuracy: 0.7283
Epoch 38/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.5401 - accuracy: 0.7254 - val_loss: 0.5095 - val_accuracy: 0.7433
Epoch 39/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.5284 - accuracy: 0.7350 - val_loss: 0.5522 - val_accuracy: 0.7617
Epoch 40/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.5496 - accuracy: 0.7308 - val_loss: 0.6106 - val_accuracy: 0.7800
Epoch 41/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.5437 - accuracy: 0.7192 - val_loss: 0.5149 - val_accuracy: 0.6933
Epoch 42/100

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480/480 [==============================] - 10s 20ms/step - loss: 0.5259 - accuracy: 0.7233

480/480 [==============================] - 10s 21ms/step - loss: 0.5259 - accuracy: 0.7233 - val_loss: 0.7799 - val_accuracy: 0.7417
Epoch 43/100

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480/480 [==============================] - 9s 20ms/step - loss: 0.5078 - accuracy: 0.7312

480/480 [==============================] - 10s 21ms/step - loss: 0.5078 - accuracy: 0.7312 - val_loss: 0.8767 - val_accuracy: 0.6733
Epoch 44/100

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480/480 [==============================] - 10s 20ms/step - loss: 0.5402 - accuracy: 0.7342

480/480 [==============================] - 10s 21ms/step - loss: 0.5402 - accuracy: 0.7342 - val_loss: 1.3711 - val_accuracy: 0.8167
Epoch 45/100

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480/480 [==============================] - 10s 20ms/step - loss: 0.5701 - accuracy: 0.7325

480/480 [==============================] - 10s 21ms/step - loss: 0.5701 - accuracy: 0.7325 - val_loss: 0.8763 - val_accuracy: 0.6567
Epoch 46/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.5411 - accuracy: 0.7246 - val_loss: 0.6292 - val_accuracy: 0.7650
Epoch 47/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.5174 - accuracy: 0.7412 - val_loss: 0.9527 - val_accuracy: 0.8033
Epoch 48/100

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480/480 [==============================] - 10s 22ms/step - loss: 0.5044 - accuracy: 0.7412 - val_loss: 0.8664 - val_accuracy: 0.7700
Epoch 49/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.5023 - accuracy: 0.7504

480/480 [==============================] - 11s 22ms/step - loss: 0.5023 - accuracy: 0.7504 - val_loss: 0.6705 - val_accuracy: 0.7617
Epoch 50/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.5111 - accuracy: 0.7462

480/480 [==============================] - 11s 22ms/step - loss: 0.5111 - accuracy: 0.7462 - val_loss: 0.9983 - val_accuracy: 0.8017
Epoch 51/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.5136 - accuracy: 0.7529

480/480 [==============================] - 10s 22ms/step - loss: 0.5136 - accuracy: 0.7529 - val_loss: 0.5716 - val_accuracy: 0.7700
Epoch 52/100

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480/480 [==============================] - 10s 20ms/step - loss: 0.4976 - accuracy: 0.7529

480/480 [==============================] - 10s 21ms/step - loss: 0.4976 - accuracy: 0.7529 - val_loss: 0.6039 - val_accuracy: 0.7433
Epoch 53/100

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480/480 [==============================] - 10s 20ms/step - loss: 0.4977 - accuracy: 0.7508

480/480 [==============================] - 10s 22ms/step - loss: 0.4977 - accuracy: 0.7508 - val_loss: 0.9086 - val_accuracy: 0.8167
Epoch 54/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.5036 - accuracy: 0.7692 - val_loss: 0.5632 - val_accuracy: 0.7500
Epoch 55/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.4789 - accuracy: 0.7679 - val_loss: 0.7640 - val_accuracy: 0.6967
Epoch 56/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.5149 - accuracy: 0.7529 - val_loss: 0.6612 - val_accuracy: 0.7617
Epoch 57/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.5051 - accuracy: 0.7396 - val_loss: 0.6453 - val_accuracy: 0.7383
Epoch 58/100

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480/480 [==============================] - 9s 20ms/step - loss: 0.5153 - accuracy: 0.7350

480/480 [==============================] - 10s 21ms/step - loss: 0.5153 - accuracy: 0.7350 - val_loss: 0.8804 - val_accuracy: 0.7817
Epoch 59/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.5083 - accuracy: 0.7608 - val_loss: 0.8647 - val_accuracy: 0.7483
Epoch 60/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.4831 - accuracy: 0.7671 - val_loss: 0.6900 - val_accuracy: 0.7900
Epoch 61/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.4720 - accuracy: 0.7758 - val_loss: 0.8021 - val_accuracy: 0.7183
Epoch 62/100

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480/480 [==============================] - 9s 20ms/step - loss: 0.4886 - accuracy: 0.7604

480/480 [==============================] - 10s 21ms/step - loss: 0.4886 - accuracy: 0.7604 - val_loss: 1.0001 - val_accuracy: 0.7433
Epoch 63/100

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480/480 [==============================] - 10s 20ms/step - loss: 0.4780 - accuracy: 0.7692

480/480 [==============================] - 10s 21ms/step - loss: 0.4780 - accuracy: 0.7692 - val_loss: 1.0847 - val_accuracy: 0.8000
Epoch 64/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.4787 - accuracy: 0.7708 - val_loss: 0.7122 - val_accuracy: 0.8050
Epoch 65/100

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480/480 [==============================] - 9s 20ms/step - loss: 0.4897 - accuracy: 0.7604

480/480 [==============================] - 10s 21ms/step - loss: 0.4897 - accuracy: 0.7604 - val_loss: 0.6187 - val_accuracy: 0.8250
Epoch 66/100

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480/480 [==============================] - 10s 20ms/step - loss: 0.4833 - accuracy: 0.7758

480/480 [==============================] - 10s 21ms/step - loss: 0.4833 - accuracy: 0.7758 - val_loss: 1.0981 - val_accuracy: 0.7750
Epoch 67/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.5226 - accuracy: 0.7529

480/480 [==============================] - 10s 22ms/step - loss: 0.5226 - accuracy: 0.7529 - val_loss: 1.0705 - val_accuracy: 0.7750
Epoch 68/100

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480/480 [==============================] - 10s 20ms/step - loss: 0.4731 - accuracy: 0.7696

480/480 [==============================] - 10s 22ms/step - loss: 0.4731 - accuracy: 0.7696 - val_loss: 1.0910 - val_accuracy: 0.8133
Epoch 69/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.4910 - accuracy: 0.7837

480/480 [==============================] - 10s 22ms/step - loss: 0.4910 - accuracy: 0.7837 - val_loss: 1.1110 - val_accuracy: 0.8033
Epoch 70/100

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480/480 [==============================] - 10s 22ms/step - loss: 0.4793 - accuracy: 0.7713 - val_loss: 1.2275 - val_accuracy: 0.8450
Epoch 71/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.4545 - accuracy: 0.7950

480/480 [==============================] - 10s 22ms/step - loss: 0.4545 - accuracy: 0.7950 - val_loss: 1.0237 - val_accuracy: 0.8183
Epoch 72/100

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480/480 [==============================] - 10s 22ms/step - loss: 0.4558 - accuracy: 0.7946 - val_loss: 1.2161 - val_accuracy: 0.7517
Epoch 73/100

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480/480 [==============================] - 10s 20ms/step - loss: 0.4480 - accuracy: 0.7900

480/480 [==============================] - 10s 21ms/step - loss: 0.4480 - accuracy: 0.7900 - val_loss: 1.1716 - val_accuracy: 0.7467
Epoch 74/100

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480/480 [==============================] - 11s 22ms/step - loss: 0.4521 - accuracy: 0.7900 - val_loss: 1.2957 - val_accuracy: 0.7067
Epoch 75/100

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480/480 [==============================] - 10s 20ms/step - loss: 0.4994 - accuracy: 0.7667

480/480 [==============================] - 10s 21ms/step - loss: 0.4994 - accuracy: 0.7667 - val_loss: 1.4301 - val_accuracy: 0.8283
Epoch 76/100

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480/480 [==============================] - 10s 20ms/step - loss: 0.4630 - accuracy: 0.7792

480/480 [==============================] - 10s 21ms/step - loss: 0.4630 - accuracy: 0.7792 - val_loss: 1.8275 - val_accuracy: 0.8067
Epoch 77/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.4597 - accuracy: 0.7796 - val_loss: 1.7814 - val_accuracy: 0.7400
Epoch 78/100

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480/480 [==============================] - 10s 20ms/step - loss: 0.4918 - accuracy: 0.7625

480/480 [==============================] - 10s 21ms/step - loss: 0.4918 - accuracy: 0.7625 - val_loss: 1.2380 - val_accuracy: 0.7700
Epoch 79/100

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480/480 [==============================] - 9s 20ms/step - loss: 0.4716 - accuracy: 0.7663

480/480 [==============================] - 10s 21ms/step - loss: 0.4716 - accuracy: 0.7663 - val_loss: 1.3961 - val_accuracy: 0.7333
Epoch 80/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.4670 - accuracy: 0.7742 - val_loss: 1.2812 - val_accuracy: 0.7400
Epoch 81/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.4485 - accuracy: 0.7929 - val_loss: 1.3302 - val_accuracy: 0.7733
Epoch 82/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.4730 - accuracy: 0.7792 - val_loss: 1.1665 - val_accuracy: 0.7783
Epoch 83/100

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480/480 [==============================] - 9s 20ms/step - loss: 0.4941 - accuracy: 0.7833

480/480 [==============================] - 10s 20ms/step - loss: 0.4941 - accuracy: 0.7833 - val_loss: 1.4579 - val_accuracy: 0.7800
Epoch 84/100

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480/480 [==============================] - 10s 20ms/step - loss: 0.4995 - accuracy: 0.7792

480/480 [==============================] - 10s 21ms/step - loss: 0.4995 - accuracy: 0.7792 - val_loss: 1.3189 - val_accuracy: 0.7650
Epoch 85/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.4558 - accuracy: 0.7875 - val_loss: 1.7166 - val_accuracy: 0.7200
Epoch 86/100

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480/480 [==============================] - 9s 20ms/step - loss: 0.4991 - accuracy: 0.7733

480/480 [==============================] - 10s 21ms/step - loss: 0.4991 - accuracy: 0.7733 - val_loss: 1.1124 - val_accuracy: 0.7733
Epoch 87/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.4540 - accuracy: 0.7892 - val_loss: 1.2964 - val_accuracy: 0.7600
Epoch 88/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.4382 - accuracy: 0.7983 - val_loss: 1.8092 - val_accuracy: 0.7567
Epoch 89/100

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480/480 [==============================] - 9s 20ms/step - loss: 0.4599 - accuracy: 0.7837

480/480 [==============================] - 10s 21ms/step - loss: 0.4599 - accuracy: 0.7837 - val_loss: 1.6273 - val_accuracy: 0.8333
Epoch 90/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.4565 - accuracy: 0.7962 - val_loss: 1.3180 - val_accuracy: 0.7633
Epoch 91/100

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480/480 [==============================] - 9s 20ms/step - loss: 0.4337 - accuracy: 0.7933

480/480 [==============================] - 10s 21ms/step - loss: 0.4337 - accuracy: 0.7933 - val_loss: 1.4957 - val_accuracy: 0.7783
Epoch 92/100

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480/480 [==============================] - 10s 20ms/step - loss: 0.4228 - accuracy: 0.7983

480/480 [==============================] - 10s 21ms/step - loss: 0.4228 - accuracy: 0.7983 - val_loss: 1.6693 - val_accuracy: 0.7917
Epoch 93/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.4349 - accuracy: 0.8133 - val_loss: 1.7342 - val_accuracy: 0.8583
Epoch 94/100

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480/480 [==============================] - 10s 20ms/step - loss: 0.4791 - accuracy: 0.7929

480/480 [==============================] - 10s 21ms/step - loss: 0.4791 - accuracy: 0.7929 - val_loss: 0.8372 - val_accuracy: 0.8500
Epoch 95/100

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480/480 [==============================] - 9s 20ms/step - loss: 0.4686 - accuracy: 0.7962

480/480 [==============================] - 10s 21ms/step - loss: 0.4686 - accuracy: 0.7962 - val_loss: 1.3008 - val_accuracy: 0.8250
Epoch 96/100

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480/480 [==============================] - 9s 20ms/step - loss: 0.4410 - accuracy: 0.8075

480/480 [==============================] - 10s 21ms/step - loss: 0.4410 - accuracy: 0.8075 - val_loss: 1.0758 - val_accuracy: 0.7967
Epoch 97/100

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480/480 [==============================] - 9s 20ms/step - loss: 0.4585 - accuracy: 0.7967

480/480 [==============================] - 10s 21ms/step - loss: 0.4585 - accuracy: 0.7967 - val_loss: 1.0703 - val_accuracy: 0.7950
Epoch 98/100

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480/480 [==============================] - 10s 21ms/step - loss: 0.4387 - accuracy: 0.8125 - val_loss: 1.1816 - val_accuracy: 0.7883
Epoch 99/100

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480/480 [==============================] - 9s 20ms/step - loss: 0.4382 - accuracy: 0.8046

480/480 [==============================] - 10s 21ms/step - loss: 0.4382 - accuracy: 0.8046 - val_loss: 1.3813 - val_accuracy: 0.8033
Epoch 100/100

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480/480 [==============================] - 9s 20ms/step - loss: 0.4173 - accuracy: 0.8108

480/480 [==============================] - 10s 21ms/step - loss: 0.4173 - accuracy: 0.8108 - val_loss: 1.8117 - val_accuracy: 0.8000

save_model_hdf5(fit_train, "../output/fit_train_dnn.h5")

Step 5: Run test on test images

tm_test = NA
feature_test <- as.matrix(dat_test[, -6007])
if(run.test_dnn){
  fit_train <- load_model_hdf5("../output/fit_train_dnn.h5")
  tm_test <- system.time({label_pred <- test_dnn(fit_train, feature_test, type = "predict_classes");
   prob_pred <- test_dnn(fit_train, feature_test, type = "predict_proba")})
}

Error in py_call_impl(callable, dots$args, dots$keywords) : 
  ValueError: Unknown config_item: GlorotNormal

evaluation

## reweight the test data to represent a balanced label distribution
label_test <- as.integer(dat_test$label)

weight_test <- rep(NA, length(label_test))
for (v in unique(label_test)){
  weight_test[label_test == v] = 0.5 * length(label_test) / length(label_test[label_test == v])
}

label_test <- ifelse(label_test == 2, 0, 1)

accu <- sum(weight_test * (label_pred == label_test)) / sum(weight_test)

# prob_pred <- apply(prob_pred, 1, max)
prob_pred <- prob_pred[, 2]

Error in prob_pred[, 2] : 量度数目不对

Summarize Running Time

Prediction performance matters, so does the running times for constructing features and for training the model, especially when the computation resource is limited.

cat("Time for constructing training features=", tm_feature_train[3], "s \n")

Time for constructing training features= 0.81 s

cat("Time for constructing testing features=", tm_feature_test[3], "s \n")

Time for constructing testing features= 0.13 s

cat("Time for training model=", tm_train[3], "s \n")

Time for training model= 1027.1 s

cat("Time for testing model=", tm_test[3], "s \n")

Time for testing model= NA s

###Reference - Du, S., Tao, Y., & Martinez, A. M. (2014). Compound facial expressions of emotion. Proceedings of the National Academy of Sciences, 111(15), E1454-E1462.

Main

Chengliang Tang, Yujie Wang, Diane Lu, Tian Zheng

Step 0 set work directories

Step 1: set up controls for evaluation experiments.

Step 2: import data and train-test split

Step 3: construct features and responses

GBM

Step 4: Train a classification model with training features and responses

Model selection with cross-validation

Step 5: Run test on test images

Summarize Running Time

XGBoost

Step 4: Train a classification model with training features and responses

Model selection with cross-validation

Step 5: Run test on test images

Summarize Running Time

DNN

Step 4: Train a classification model with training features and responses

Model selection with cross-validation

Step 5: Run test on test images

Summarize Running Time